⚡ Nano-Models for Temporal AI: Pieces’ LTM-2.5 Breakthrough
Latency. Privacy. Cost. Until recently, you had to choose two.
When you're dealing with long-term memory for intelligent systems, especially at the OS level, there’s a painful truth: just identifying when to look can cost more compute (and user trust) than finding the info itself.
Most pipelines offload that problem to cloud LLMs — parsing user intent, generating time spans, normalizing input, scoring relevance, etc. That adds seconds of latency, cloud costs that scale with token volume, and worst of all, exposes highly personal context in transit.
🧠 The Breakthrough: LTM-2.5
We recently dropped a breakthrough: two nano-models, trained via distillation, quantized, pruned, and optimized to run directly on consumer hardware.
- The first model figures out if a query involves time, and if so, what kind: “What was I working on just now?” vs. “What am I doing tomorrow?”
- The second model extracts the exact time span(s) implied by user language. Think “just before lunch yesterday” or “sometime last summer.”
Together, they replace a 10–15 step cloud pipeline, reducing latency to milliseconds, keeping all data on-device, and removing reliance on remote inference altogether.
🛠️ Why It Works
- Intent classifier: >99% accuracy, real-time inference on consumer CPUs
- Span predictor: high IoU & coverage even for fuzzy or implied queries
- Runs completely offline — zero token cost, zero cloud dependency
No orchestration, no round trips, no privacy compromises.
🔍 What It Unlocks
- Point-in-time recall: “What was I just doing?”
- Temporal search: “Show me last week around Friday”
- Scheduling vs. retrieval differentiation
- Smart timeline navigation without scanning the full corpus
And that’s just for temporal memory. This is one of 11 nano-models inside LTM-2.5 — all working toward intelligent, privacy-first memory at the OS layer.
We open-sourced some of the architecture and benchmarks — check it all out in the full breakdown here →
👉 Read the full deep dive
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